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Are socio-demographic and economic characteristics good predictors of misinformation during an epidemic?

ABSTRACT: We combine data on beliefs about the origin of the 2014 Ebola outbreak with two supervised machine learning methods to predict who is more likely to be misinformed. Contrary to popular beliefs, we uncover that, socio-demographic and economic indicators play a minor role in predicting those who are misinformed: misinformed individuals are not any poorer, older, less educated, more economically distressed, more rural, or ethnically different than individuals who are informed. However, they are more likely to report high levels of distrust, especially towards governmental institutions. By distinguishing between types of beliefs, distrust in the central government is the primary predictor of individuals assigning a political origin to the epidemic, while Muslim religion is the most important predictor of whether the individual assigns a supernatural origin. Instead, educational level has a markedly higher importance for ethnic beliefs. Taken together, the results highlight that government trust might play the most important role in reducing misinformation during epidemics.

SUBMITTER: Maffioli EM

PROVIDER: S-EPMC10022074 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Are socio-demographic and economic characteristics good predictors of misinformation during an epidemic?

Maffioli Elisa M EM Gonzalez Robert R

PLOS global public health 20220316 3

We combine data on beliefs about the origin of the 2014 Ebola outbreak with two supervised machine learning methods to predict who is more likely to be misinformed. Contrary to popular beliefs, we uncover that, socio-demographic and economic indicators play a minor role in predicting those who are misinformed: misinformed individuals are not any poorer, older, less educated, more economically distressed, more rural, or ethnically different than individuals who are informed. However, they are mor ...[more]

PMID: 36962368

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Project description:IntroductionEmpowerment is considered pivotal for how women access and use health care services and experience their sexual and reproductive rights. In Mozambique, women's empowerment requires a better understanding and contextualization, including looking at factors that could drive empowerment in that context. This study aims to identify socioeconomic, demographic, and behavioural determinants of different domains of women's empowerment in Mozambique.MethodsUsing the Demographic and Health Survey (DHS) conducted in 2015 for Mozambique, a sample of 2072 women aged between 15 and 49 years old were included in this study. The DHS's indicators of women's empowerment were used in a principal component analysis and the obtained components were identified as the domains of empowerment. Logistic regressions were run to estimate the association of socioeconomic, demographic, and behavioural characteristics with each domain of empowerment. Crude and adjusted odds ratios (OR) and respective 95% confidence intervals (95% CI) were calculated.ResultsThree domains of women's empowerment were identified, namely (1) Beliefs about violence against women, (2) Decision-making, and (3) Control over sexuality and safe sex. Region, rurality, the experience of intimate partner violence (IPV) and partner's controlling behaviours were associated with Beliefs about violence against women, while Decision-making and Control over sexuality and safe sex were also associated with education, age and wealth. Employment, polygamous marriage and religion was positively associated with Decision-making, and access to media increased the odds of Control over sexuality and safe sex.ConclusionWomen's empowerment seems to be determined by different socio-economic, demographic, and behavioural factors and this seems to be closely related to different domains of empowerment identified. This finding affirms the multi-dimensionality of empowerment as well as the importance of considering the context- and community-specific characteristics.

Dataset Information

Are socio-demographic and economic characteristics good predictors of misinformation during an epidemic?

Publications

Are socio-demographic and economic characteristics good predictors of misinformation during an epidemic?

Similar Datasets

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